Computational prediction of Drug-Disease association based on Graph-regularized one bit Matrix completion

Abstract

MotivationInvestigation of existing drugs is an effective alternative to discovery of new drugs for treating diseases. This task of drug re-positioning can be assisted by various kinds of computational methods to predict the best indication for a drug given the open-source biological datasets. Owing to the fact that similar drugs tend to have common pathways and disease indications, the association matrix is assumed to be of low-rank structure. Hence, the problem of drug-disease association prediction can been modelled as a low-rank matrix-completion problem.ResultsIn this work, we propose a novel matrix completion framework which makes use of the sideinformation associated with drugs/diseases for the prediction of drug-disease indications modelled as neighborhood graph: Graph regularized 1-bit matrix compeltion (GR1BMC). The algorithm is specially designed for binary data and uses parallel proximal algorithm to solve the aforesaid minimization problem taking into account all the constraints including the neighborhood graph incorporation and restricting predicted scores within the specified range. The results of the proposed algorithm have been validated on two standard drug-disease association databases (Fdataset and Cdataset) by evaluating the AUC across the 10-fold cross validation splits. The usage of the method is also evaluated through a case study where top 5 indications are predicted for novel drugs and diseases, which then are verified with the CTD database. The results of these experiments demonstrate the practical usage and superiority of the proposed approach over the benchmark methods.Contactaanchalm@iiitd.ac.in

A computational approach to aid clinicians in selecting anti-viral drugs for COVID-19 trials

“…• Graph regularized frameworks (GRMF: graph regularized matrix factorization [20], GRMC: graph regularized matrix completion [39], GRBMC: graph regularized binary matrix completion [37])…”

“…The minimization with respect to X can be solved by making use of the PPXA (parallel proximal algorithm) [44]. Such approach allows to decouple the constraints by introducing proxy variables and then solving each subproblem in a parallel fashion as shown in [37] (referred as GRBMC here).…”

DeepVir - Graphical Deep Matrix Factorization for In Silico Antiviral Repositioning: Application to COVID-19

“…• Drug Disease association prediction: One predicts the probability that a certain drug will interact with a disease or not in a partially filled drug-disease association matrix. [84,46,81,48].…”

“…It was found that graph regularised techniques, including similarities based on genomic structure of viruses and chemical structure of antiviral drugs, performed better than the non-regularised competitors. The most successful techniques were graph regularized versions of matrix completion [51], binary matrix completion [48] and (shallow) matrix factorization [25].…”